Hedge fund database weaknesses exposed in research

By: Jonathan Boyd | 27 Mar 2012

Commonly used hedge fund databases suffer problems of bias, missing data and limitations in identifying correlations between factors such as age and size of funds and their performances, according to academic research from the UK and Finland.

Published jointly by London’s Imperial College Business School and the University of Oulu, the latest draft of the paper – Revisiting stylised facts about hedge funds – illustrates that there is significant statistical impact on performance ‘persistence’ caused not only by factors such as share restrictions, rebalancing frequency, fund size and weightings, but also choice of database in the form of “database biases”

“Since several stylised facts are sensitive to the choice of the database it is important to use a high quality consolidated database such as the one used in this paper,” the authors note.

Their research brought together five databases: TASS, HFR, BarclayHedge, EurekaHedge and Morningstar. However, they found that the overlap across all five was just 3.7%.

This is an important finding, said Robert Kosowski, director Risk Management Laboratory at Imperial and co-author of the report, because it leads to questions over just which database is being used when, for example, fund of hedge funds approach their investible universe, or when politicians make statements about further regulation of alternative investments.

The findings lead to other questions, such as whether it is possible to generate a top-five funds list consistently across the different databases over time. For example, the research found that coverage of smaller funds varied significantly between the databases; those with fewer smaller funds had weaker average performance figures. This finding is coupled with that of younger funds in the databases tending to perform better than older ones.

Another problem is so-called ‘backfill bias’, or the rate at which the databases add in historical data on funds in their lists. Both backfill bias and AUM data differences in turn lead to differences in the way value-weighted and equal-weighted indices may work.

On the basis of the findings the research concludes that those looking to draw inference about hedge fund performance should aggregate the different databases available.